Systems and methods for sea state prediction and automated vessel navigation

ABSTRACT

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional. Application61/355,064 filed Jun. 15, 2010 and U.S. Provisional. Application61/474,839 filed Apr. 13, 2011, the disclosures of which areincorporated herein by reference.

STATEMENT OF FEDERAL SUPPORT

The invention described herein was made in the performance of work undera NASA contract, and is subject to the provisions of Public Law 96-517(35 U.S.C. §202) in which the Contractor has elected to retain title.

FIELD OF THE INVENTION

The present invention is generally related to sea state prediction andautomated vessel navigation, and more specifically to sea stateprediction by detecting wave fronts and automated vessel navigationbased upon main wave direction.

BACKGROUND

Vessel navigation on fluid surfaces such as the sea can be moredifficult than vehicle navigation on rigid surfaces such as on land.Waves on fluid surfaces apply significant force to vessels, inparticular when such vessels are moving with high speeds in severe seaconditions. Severe sea conditions are sea conditions with significantwave activity. Severe sea conditions can be acutely fell in littoraloperations, or operations close to shore, where the shallow water andunderwater geography can create significant wave lengths and speeds.Vessels that can command high speeds or that are of a small mass areespecially vulnerable to damage from severe sea conditions. This damagecaused by kinetic wave energy includes crew injuries, capsizing, bowdiving or other damage to the vessel. Furthermore, waves can slow avessel down from reaching its target destination. Severe sea conditionsare not limited to conditions at sea but can apply to conditions on anybody of water including fresh or salt water as well as an ocean, lake orriver.

The difficulty of navigating a vessel in severe sea conditions isfurther compounded for autonomous vessel navigation. Difficulty canarise where vessel navigation depends upon the limited resources ofmachine sensory equipment and processing to navigate the vessel.

SUMMARY OF THE INVENTION

Systems and methods in accordance with embodiments of the inventionperform sea state prediction and/or autonomous navigation. Oneembodiment of the invention includes a method of predicting a future seastate including generating a sequence of at least two 3D images of a seasurface using at least two image sensors, detecting peaks and troughs inthe 3D images using a processor, identifying at least one wavefront ineach 3D image based upon the detected peaks and troughs using theprocessor, characterizing at least one propagating wave based upon thepropagation of wavefronts detected in the sequence of 3D images usingthe processor, and predicting a future sea state using the at least onepropagating wave characterizing the propagation of wavefronts in thesequence of 3D images using the processor.

In a further embodiment, generating a sequence of at least two 3D imagesof a sea surface uses two pairs of image sensors.

In another embodiment, generating a sequence of at least two 3D imagesof a sea surface also includes using a radar sensor in combination withthe at least two image sensors.

In a still further embodiment, images sensors capture black and whiteimages.

In a still another embodiment, image sensors capture color images.

In a yet further embodiment, detecting peaks and troughs includes usinga random sampling process.

In a yet another embodiment, detecting peaks and troughs includes usinga least means squares process.

In a further embodiment again, detecting peaks and troughs includesusing a thresholding process.

In another embodiment again, identifying at least one wavefront includesusing a nearest neighbor process.

In a further additional embodiment, characterizing at least onepropagating wave includes determining the amplitude, frequency andvelocity of at least one propagating wave.

In another additional embodiment, characterizing at least onepropagating wave includes determining the direction of at least onepropagating wave.

In a still yet further embodiment, characterizing at least onepropagating wave includes determining the wave phase of at least onepropagating wave.

A still yet another embodiment further includes autonomously navigatinga vessel based upon a predicted future sea state.

A still yet another embodiment includes a method of autonomous vesselnavigation based upon a predicted sea state and target location,including determining at least one subtarget based upon target locationand predicted sea state using a macronavigation system, communicating atleast one subtarget to the micronavigation system, and controlling avessel to navigate toward at least one subtarget using a micronavigationsystem.

In a still further embodiment again, a predicted sea state includes amain wave direction.

In still another embodiment again, a predicted sea state includes a wavephase information.

In a still further additional embodiment, a subtarget is the targetlocation.

In still another additional embodiment, a micronavigation systemincludes a proportional integral derivative controller.

In a yet further embodiment again, navigating toward at least onesubtarget causes the vessel to tack relative to a main wave direction.

In a yet another embodiment again, a subtarget causes the vessel tonavigate at 60 degrees relative to the main wave direction.

In a yet further additional embodiment, controlling the vessel includescontrolling the throttle and the rudder of the vessel.

In a yet another additional embodiment, a predicted sea state ispredicted using at least one propagating wave characterizing propagationof wavefronts from detected peaks and troughs in a sequence of 3Dimages.

A further additional embodiment again includes a system for predicting afuture sea state including a sensor system configured to captureinformation concerning the shape of the sea surface, a sea stateprocessor configured to communicate with a sensor system, wherein thesensor system and the sea state processor are configured so thatcaptured information is used to generate a sequence of 3D images of asea surface, wherein the sea state processor is configured to detectpeaks and troughs in the 3D images, identify at least one wavefront ineach 3D image based upon the detected peaks and troughs, characterize atleast one propagating wave based upon the propagation of wavefrontsdetected in the sequence of 3D images using the processor, and predict afuture sea state using at least one propagating wave characterizing thepropagation of wavefronts in the sequence of 3D images using theprocessor.

In another additional embodiment again, a sensor system includes twopairs of image sensors.

In another further embodiment again, a sensor system includes a radarsensor.

In another further embodiment, a sensor system and a sea state processorare configured so that captured information is used to generate asequence of black and white 3D images of a sea surface.

In still another further embodiment, a sensor system and a sea stateprocessor are configured so that captured information is used togenerate a sequence of color 3D images of a sea surface.

In yet another further embodiment, a sea state processor is configuredto detect peaks and troughs using a random sampling process.

In another further embodiment again, a sea state processor is configuredto detect peaks and troughs using a least means squares process.

In another further additional embodiment, a sea state processor isconfigured to detect peaks and troughs using a thresholding process.

In still yet another further embodiment, a sea state processor isconfigured to identify at least one wavefront using a nearest neighborprocess.

In another yet additional embodiment, a sea state processor isconfigured to characterize at least one propagating wave by theamplitude, frequency and velocity of the at least one propagating wave.

In another yet further embodiment, a sea state processor is configuredto characterize at least one propagating wave by the direction of the atleast one propagating wave.

In another yet additional embodiment again, a sea state processor isconfigured to characterize at least one propagating wave by the wavephase of the at least one propagating wave.

Another yet further embodiment again includes an autonomous vesselnavigation system that utilizes the predicted future sea state todetermine vessel heading when navigating toward a target.

A further embodiment includes an autonomous vessel navigation system,including a macronavigation system configured to receive a predicted seastate and a target as inputs and to generate a subtarget as an output,and a micronavigation system configured to receive a subtarget as aninput and to generate vessel control signals as outputs, wherein themacronavigation system is configured to determine at least one subtargetbased upon the target location and the predicted sea state, andcommunicate at least one subtarget to the micronavigation system, andwherein the micronavigation system is configured to generate vesselcontrol signals that head a vessel toward at least one subtarget.

In a yet further additional embodiment, a predicted sea state includes amain wave direction.

In a further additional again embodiment, a predicted sea state includeswave phase information.

In another further embodiment again, a subtarget is a target location.

In yet another further embodiment again, a micronavigation systemincludes a proportional integral derivative controller.

In a further embodiment, a macronavigation system is configured to causea vessel to tack relative to a main wave direction.

In yet a further embodiment, a macronavigation system is configured todetermine at least one subtarget that causes vessel navigation at 60degrees relative to a main wave direction

In a yet further embodiment again, a micronavigation system isconfigured to generate vessel control signals configured to control thethrottle and the rudder of a vessel.

In another further embodiment yet again, a macronavigation system isconfigured to receive a predicted sea state from a system for predictinga future sea state.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system diagram of a sea state prediction andautonomous vessel navigation system in accordance with an embodiment ofthe invention.

FIG. 2 is a flow chart illustrating a process for autonomous vesselnavigation involving the detection of wave fronts in accordance with anembodiment of the invention.

FIG. 3 is a flow chart illustrating a process for detecting wave frontsfrom a 3D map of a current sea state in accordance with an embodiment ofthe invention.

FIG. 4 is a flow chart illustrating a process for autonomous vesselnavigation using a target, main wave direction and wave phase predictionin accordance with an embodiment of the invention.

FIG. 5 illustrates a control framework for an autonomous vesselnavigation system utilizing a macronavigation system and amicronavigation system in accordance with an embodiment of theinvention.

FIG. 6 illustrates a control framework for a macronavigation system inaccordance with an embodiment of the invention.

FIG. 7 illustrates a determination of a subtarget from an appropriatepoint of sail given a target position and main wave direction inaccordance with an embodiment of the invention.

FIG. 8A illustrates experimental results for bow-diving as a function ofthe maximal point of sail for head seas in accordance with an embodimentof the invention.

FIG. 8B illustrates experimental results for bow diving as a function ofthe maximal point of sail for following seas in accordance with anembodiment of the invention.

FIG. 9A illustrates experimental results for a stand-alone PIDcontroller compared to the PID controller configured to tack using amacronavigation system in accordance with an embodiment of the inventionwith bow diving as a function of the wave height H.

FIG. 9B illustrates experimental results for a stand-alone PIDcontroller compared to the PID controller configured to tack using amacronavigation system in accordance with an embodiment of the inventionwith time of travel. T as a function of the target direction d.

FIG. 10 illustrates the trajectories generated by an autonomousnavigation system that utilizes tacking to navigate to a target 100meters away in 18 different directions in accordance with an embodimentof the invention.

FIG. 11 illustrates the difference between normalized heave responsebetween vessel navigation with an autonomous navigation system inaccordance with an embodiment of the invention and one without theautonomous navigation system for a 13 meter high speed vessel operatingat 35 knots over a range of sea states.

DETAILED DESCRIPTION

Turning now to the drawings, systems and methods for automated vesselnavigation using predictions of sea state conditions are illustrated. Invarious embodiments, sensors capture 3D maps of the sea surface. Fromthe 3D maps, a model for the sea state is found from which sea statepredictions are made. Sea state predictions can be made by detecting thepeaks and troughs of waves from a noisy signal. Using the sea statepredictions, a vessel can be autonomously navigated to minimize theforces experienced by the vessel and its crew associated with severe seaconditions. In this way, autonomous navigation systems in accordancewith embodiments of the invention can improve the speed with which avessel can reach a target in severe sea conditions, while reducing thelikelihood of damage to the vessel and/or crew. In many embodiments, theautonomous navigation system utilizes a number of subtargets calculatedbased upon a main wave direction (derived from a predicted sea state)and a target location. The vessel speed, heading and load balancingstate can be adjusted to reach the subtargets during the autonomousnavigation. Thereby, utilizing only machine sensory input, a vessel canautonomously navigate severe sea conditions in a way that limits theforces and/or damage experienced by the craft and crew.

In many embodiments, autonomous sea surface navigation utilizes 3D mapswhere features of the sea state are extracted. Sea state featuresinclude any potential hazards such as debris on the water or strongcurrents or waves on the sea surface. A sequence of 3D images of the seasurface can be analyzed to predict future sea states. Sea states can bedetermined by modeling the observed undulations in the sea surface asbeing caused by one or more propagating planar waves. In severalembodiments, the sea state prediction involves locating peaks andtroughs in a sequence of 3D images in order to the propagation ofwavefronts. The wavefronts can then be used to estimate the amplitude,frequency and velocity of each observed propagating wave. In a number ofembodiments, peaks and troughs of waves can be found through statisticalmethods that determine high and low points within a 3D map that can thenbe grouped to identify wavefronts. Once the amplitude, frequency andvelocity of the waves are determined, predictions can be made concerningthe sea state at a time in the future. In many embodiments, the seastate prediction is utilized to derive safe paths to a target that limitthe forces experienced by the craft or crew.

In several embodiments, autonomous navigation systems determine paths toa target utilizing navigation techniques including tacking bydetermining the vessel's point of sail relative to wave direction (asopposed to wind direction). Tacking is a maneuver employed in sailing bywhich a vessel turns its bow through the wind so that the direction fromwhich the wind blows changes from one side to the other. Similarly, forwave direction, tacking is a maneuver by which a vessel turns its bowthrough the wave so that the direction of the forward force of the wavechanges from one side of the vessel to the other. Points of sailtypically describe a sailing boat's course in relation to the winddirection, such as by utilizing tacking to sail into the wind. Using thepoints of sail analogy with respect to wave direction, the term point ofsail is used here to describe the direction in which a vessel navigatesrelative to wave direction. In particular embodiments, an autonomousnavigation system constantly updates the heading of the vessel inresponse to the dynamic nature of the sea surface.

Systems and methods for performing sea state prediction and automatedvessel navigation using sea state predictions in accordance withembodiments of the invention are discussed further below.

System Architecture

Autonomous navigation systems in accordance with many embodiments of theinvention include a system where sensors, processors and controllersfunction to predict a sea state and to provide navigation instructionsto a vessel. FIG. 1 illustrates a system diagram of a sea stateprediction and autonomous vessel navigation system 100 in accordancewith an embodiment of the invention. The system 100 includes a radarsensor 102 and camera sensors 104. These sensors are connected to a seastate processor 106, vessel navigation processor 108 and vesselnavigation controller(s) 110 via a network 112.

In the illustrated embodiment, the camera sensors (104) are configuredas a stereo pair and can be utilized in combination with the radarsensor (102) to create a sequence of 3D images of the sea state. These3D images of the sea state can be processed by a sea state processor topredict future sea states. In certain embodiments, the sea stateprocessor can model the sea state for future predictions by detectingmultiple wavefronts over time to estimate the amplitude, frequency andvelocity of the wavefronts. In various embodiments, wavefronts aredetermined by detecting the peaks and troughs in a noisy signal throughmethods including (but not limited to) high and low point clusters onthe 3D images of the sea state. The detection of wavefronts inaccordance with embodiments of the invention is discussed below.

In the illustrated embodiment, the sea state prediction and autonomousvessel navigation system 100 navigates the vessel based upon the seastate predictions. In certain embodiments, information related to a mainwave direction or wave phase is used to determine a tack plan orsubtarget location as part of a navigation plan to reach a predeterminedtarget. Vessel speed, heading and load balancing in view of the targetcan then be updated by the sea state prediction and autonomous vesselnavigation system 100.

In many embodiments, the system is implemented locally on a vessel orcan utilize remote computing resources. The sensors can be any kind ofsensor capable of producing 3D images of a sea surface. In addition, thesea state processor and vessel navigation processor can be implementedusing the same physical computing system or on different processors.Vessel navigation controllers can be any controller that is used tocontrol vessel navigation. In many embodiments, the sea state predictionand autonomous vessel navigation system is implemented using a Control.Architecture for Robotic Agent Command and Sensing (CARACaS)manufactured by Spatial. Integrated Systems, Inc. of Virginia Beach, Va.

Processes related to a sea state prediction and autonomous vesselnavigation in accordance with embodiments of the invention are discussedfurther below.

Sea State Prediction and Autonomous Navigation

As the sea surface changes, sea state predictions can be utilized tofacilitate autonomous navigation to targets. FIG. 2 is a flow chartillustrating a process 200 for performing sea stat prediction for use inautonomous vessel navigation in accordance with an embodiment of theinvention. The process 200 begins by detecting (202) wave fronts in aninitial 3D image. After detecting (202) initial wave fronts, themovement of the wave fronts can be tracked by detecting (204) wavefronts in subsequent 3D images. Based upon the location of wave frontsin the sequence of 3D images, the amplitude, frequency and velocity ofthe wavefronts can be estimated (206). After the characteristics of theobserved waves are estimated (206), sea state predictions (208) can bemade by modeling the propagation of the observed waves at a point oftime in the future. Based upon the sea state prediction (208), thesystem can plan (210) a course toward a target destination. After thesystem begins to navigate (210) along the course to the targetdestination, a decision (212) is made as to whether the target has beenreached. If the target is not yet reached, the system continues tocollect information used to refine sea state predictions and to navigateto the target based upon the sea state predictions. If the target isalready reached, then the process ends.

In many embodiments, initial wave fronts are detected by determiningpeaks and troughs in a noisy signal thorough statistical analysis ofhigh and low point clusters. The initial wave fronts can be observed byanalyzing a single 3D map of a sea state or multiple 3D maps of a seastate captured from different perspectives and then fused or processedin a similar manner that exploits the redundancy in the images. Similaranalysis can be utilized to detect wavefronts in subsequent 3D map(s) ofthe sea surface.

In several embodiments, sea state predictions can be made based upon theexpected behavior of multiple wave fronts modeled as planar waves.Tracking the waves from one image or 3D map of the sea surface to thenext can enable the determination of the amplitude, frequency andvelocity of the waves. Once these parameters are known for each wave,future sea states can be predicted.

In a multitude of embodiments, navigation toward a target can beaccomplished by navigating toward a subtarget dependent upon the seastate and the target location. The subtarget allows for vesselnavigation to adapt to changing sea states. Autonomous navigationsystems that utilize information obtained from sea state predictions inaccordance with embodiments of the invention are discussed furtherbelow. Prior to discussing autonomous navigation systems, however,processes related to wave front detection in accordance with embodimentsof the invention are discussed.

Wave Front Detection

Wave fronts can be detected by processing information from a 3D map ofthe sea surface. FIG. 3 is a flow chart illustrating a process 300 fordetecting wave fronts from a 3D map of the sea surface in accordancewith an embodiment of the invention. The process 300 begins by obtaining(302) a 3D map of the sea surface. After obtaining (302) the 3D map,peaks and troughs on the 3D map are detected (304) within the noisysignal. After detecting (304) the peaks and troughs, the wavefronts arethen detected (306).

A machine vision system can generate a sequence of images that can beused to generate a 3D map of how sea state changes over time. The systemcan use one or more stereo camera systems and the sensor data from thestereo camera systems can be combined with additional information fromother types of sensors including but not limited to radar systems.Certain systems use two pairs of cameras to obtain stereo 3D images thatare then fused. Several systems use black and white cameras, althoughcolor cameras or a combination of color and black and white can be usedin other systems. In many embodiments, radar information is fused withinformation captured by stereo camera systems to obtain better low lightperformance and to increase the range of the 3D map. In otherembodiments, any of a variety of sensor systems that can generate 3Dimages can be utilized including but not limited to array cameras.

In a variety of embodiments, peaks and troughs can be detected in anoisy signal through signal processing. A sea state detection system cantake a sequence of 3D images of the sea surface over a short period oftime. A sequence of high and low point clusters can be detected acrossthe 3D images using a number of different processes including but notlimited to the RANdom SAmple Consensus or RANSAC process, a least meanssquare process, and/or thresholding.

3D images of a sea surface generate a significant amount of noise as thewater is dynamic and reflections in the water can be difficult forsystems to handle. A number of methods can also be used to filler noisefrom the captured 3D images. The RANSAC process is an iterative methodto estimate parameters of a model from a set of observed data, whichcontains outliers, or data that does not fit a model. RANSAC can be usedto filler out noise through an assumption that given a set of inliers,there is a procedure which can estimate the parameters of a model thatoptimally explains or fits the data. The least means square ELMS)process finds the coefficients for a specific function that produces theleast mean square of the error signal, or the difference between thedesired and the actual signal. Thresholding is a process whereby peaksand troughs are identified based upon a value being above or below aspecified threshold. Although specific processes for detecting peaks andtroughs are disclosed above, any of a variety of processes can beutilized in accordance with embodiments of the invention.

In numerous embodiments, wavefronts can be detected by analyzing thepeaks and troughs. Once the peaks and troughs are detected, then theycan be grouped together to locate the wave fronts of planar waves on thewater. The nearest neighbor algorithm can be used to identify wavefronts from clusters of peaks or clusters of troughs. The nearestneighbor algorithm classifies an object by a majority of its neighbors,with the object being assigned to the class most common amongst itsnearest neighbors. In other embodiments, any of a variety of algorithmscan be utilized to group peaks and troughs to identify wavefronts.

Although specific processes are described above for detecting wavefrontswithin 3D images of a sea surface. Any of a variety of processes thatare capable of detecting planar waves in sequences of captured 3D imagescan be utilized in accordance with embodiments of the invention. Oncewavefronts have been detected, a sea state prediction can be performedand a vessel can autonomously navigate relying upon the sea stateprediction. Processes related to autonomous vessel navigation inaccordance with embodiments of the invention are discussed furtherbelow.

Autonomous Vessel Navigation

Autonomous vessel navigation systems can utilize sea state predictionsto improve system performance in any of a number of ways including (butnot limited to) attempting to limit the forces experienced by a vesseland/or its crew in severe sea states. In many embodiments, the sea statemay be sufficiently severe as to warrant navigating to avoid waves asopposed directly toward a target. FIG. 4 is a flow chart illustrating aprocess 400 for autonomous vessel navigation using a target, and mainwave direction in accordance with an embodiment of the invention. Theprocess 400 begins by determining (402) the main wave direction from thepredicted sea state. After determining (402) the main wave direction, apath to the target is developed (404). In several embodiments, planningthe path to the target can utilize the predicted phase of the main waveobtained using a sea state prediction to time turns relative to thephase of the wave. After determining (404) a path to the target, asubtarget is determined (406) and the vessel speed, heading and loadbalancing are updated (408) to steer the vessel toward the subtarget.Autonomous vessel navigation systems in accordance with embodiments ofthe invention are discussed further below.

Autonomous Navigation Control Framework

Autonomous vessel navigation can be facilitated using a controlframework. FIG. 5 illustrates a control framework 500 for autonomousvessel navigation utilizing a macronavigation system and amicronavigation system from target and sensory information in accordancewith an embodiment of the invention. In the control framework, both thetarget 502 and sensor 504 feeds into the macronavigation system 506. Themacronavigation system 506 feeds into the micronavigation system 508,which can be a proportional-integral-derivative controller (PIDcontroller). In the illustrated embodiment, the micronavigation system508 feeds into both the throttle 510 and the rudder 512 of the vessel.The status of the throttle 510 and rudder 512 of the vessel effects asea state prediction or real world conditions 514. The sea stateprediction or the real world conditions 514 then affects the target 502and the sensor 504.

In many embodiments, macronavigation processes determine the wayautonomous navigation occurs while micronavigation processes determinehow the autonomous navigation strategy should be implemented. Both thetarget and the sensory data are used by the macronavigation system toplot the best navigable path to the target given the observed sea state(obtained using the sensor system). In certain embodiments, themacronavigation system masks the target position and calculatessubtargets that are provided to the micronavigation system so that thevessel does not head directly toward the actual target. In this way, themacronavigation system can implement tacks and/or other maneuvers thatcan limit the forces experienced by the vessel and its crew. Themicronavigation controls the throttle and rudder of the vessel to headthe vessel toward the target or subtarget provided to themicronavigation system by the macronavigation system. After the throttleand rudder states are updated, new sea state predictions can be used bythe autonomous navigation system to update the path the vessel takes tothe target.

In certain embodiments, for reliable target reaching, a PID controlapproach is used by the micronavigation system. A PID controller is ageneric control loop feedback mechanism that calculates an “error” valueas the difference between a measured process variable and a desired setpoint. The controller attempts to minimize the error by adjusting theprocess control input. In many embodiments, the PID controller drivesthe throttle strength and position based on the current target (orsubtarget) position provided to the micronavigation system relative tothe vessel. The throttle strength is proportional to the absolute targetdistance, and the rudder command is proportional to the targetdirection. In calculations for particular embodiments, t(t)=(x,y) is thetarget position and p(t) is the current vessel position in worldcoordinates at time t. Then, the control deviation is c(t)=t(t)−p(t).The control deviation is translated into command signals according tothe following equation for the throttle strength:

t(t)=P _(t) ∥e(t)∥+I _(t)∥∫_(O) ^(t) e(t′)dt′∥+D _(t) ∥e(t)−e(t−1)∥

The control deviation is translated into command signals according tothe following equation for the rudder position, where e_(y) is thecontrol deviation that points sideways to starboard along the y-axis ofthe vessel according to the coordinate system as seen by the vessel:

r(t)=P _(r) e _(y)(t)+I _(r)∫_(O) ^(T) e _(y)(t′)dt′+D _(r) |e _(y)(t)−e_(y)(t−1)|

Targets behind the vessel require a turn of the vessel. This can easilybe achieved by multiplying throttle and rudder commands withsign(e_(x)(t)) and with sign(c_(x)(t)), respectively. In a number ofembodiments, the following gain constants can be used: P_(t)=0.03;I_(t)=D_(t)=P_(r)=0.02, I_(r)=0 and D_(r)=0.01. The throttle and ruddercommands can be restricted to a reasonable range by clipping theirvalues to [−0.25, 0.7] and [−0.4, 0.4]. In other embodiments, any of avariety of micronavigation systems can be utilized including PID controlsystems that incorporate alternative parameters and/or constraints.

As discussed above, the subtargets utilized by micronavigation systemsare generated by macronavigation systems in accordance with embodimentsof the invention. FIG. 6 illustrates a control framework 600 formacronavigation in accordance with an embodiment of the invention. Inthe control framework 600, a target 502 is used to determine (610) thedirection from the vessel to the target. The target is also provided toa navigation planner 612. Sensor data 504 is used to determine (614) amain wave direction. In many embodiments, the main wave direction can bedetermined using a sea state prediction process similar to any of thesea state prediction processes outlined above. The difference betweenthe target direction 610 and the main wave direction 614 determines(616) the point of sail of the vessel relative to the main wave. Thepoint of sail is fed into the navigation planner 612, which generates asubtarget that is provided to the micronavigation system 508. As isdiscussed further below, the macronavigation system illustrated in FIG.6 can be utilized to incorporate tack planning into a macronavigationprocess in accordance with embodiments of the invention.

Tack Planning

In several embodiments, tacking behavior constrains vessel motion tominimize the effects of severe sea conditions, such as bow diving. Bowdiving is when a vessel's bow is submerged due to either the motion ofthe boat or the sea surface. In many embodiments, tack planning is usedto determine subtargets in autonomous vessel navigation to limit theforces that are experienced by the vessel and/or crew as it navigatestoward a target.

Referring back to the macronaviagtion system illustrated in FIG. 6,knowledge about the point of sail (POS) necessary to reach a target isan important part of implementing a tack planner. In certainembodiments, the POS is calculated depending on the target direction(TD) and the main wave direction (MWD) by POS=TD−MWD. Thus, aprerequisite for the planner is the estimation of the MWD. Whilederivation of the target direction from the target position isstraightforward, the main wave direction is extracted from sensoryinformation.

Macronavigation systems in accordance with many embodiments of theinvention tack at certain angles to minimize bow diving. FIG. 7illustrates a determination of a subtarget from an appropriate point ofsail given a target position and main wave direction in accordance withan embodiment of the invention. In the illustrated embodiment, when thepoint of sail necessary to reach the target is too steep with respect tothe main wave direction, the planner determines that a subtarget that isthe projection of the original target onto the appropriate point ofsail.

FIG. 8A illustrates experimental results for bow diving as a function ofthe maximal point of sail for head seas in accordance with an embodimentof the invention. Likewise, FIG. 8B illustrates experimental results forbow diving as a function of the maximal point of sail for following seasin accordance with an embodiment of the invention. As illustrated inboth FIGS. 8A and 8B, bow diving is significantly reduced around a tackof 60 degrees in both head and following seas. Thereby in certainembodiments, tack planning can take advantage of tacking at around 60degrees.

Generally, points of sail in parallel or antiparallel to the MWD resultin considerable bow diving. The navigation planner creates subtargetsfor tacks according to a safe point of sail p_(opt), when the point ofsail p necessary to reach a target falls into one of the followingranges:

$p_{opt} = \left\{ \begin{matrix}{- p_{\max}^{head}} & {{{if}\mspace{14mu} - p_{\max}^{head}} \leq p \leq 0} \\p_{\max}^{head} & {{{if}\mspace{14mu} 0} \leq p \leq p_{\max}^{head}} \\{180 - p_{\max}^{follow}} & {{{{if}\mspace{14mu} 180} - p_{\max}^{follow}} \leq p \leq 180} \\{180 + p_{\max}^{follow}} & {{{if}\mspace{14mu} 180} \leq p \leq {180 + p_{\max}^{follow}}}\end{matrix} \right.$

A subtarget is created that is the projection of the original targetonto the adopted point of sail p_(opt). In doing so, the navigationplanner transfers the direct path to the target into a tacked path thatcircumvents extreme points of sail with the risk of bow diving. Invarious embodiments, the concept of subtarget generation is illustratedin FIG. 7. Certain embodiments yield ideal values for p_(max) ^(head)and p_(max) ^(follow) where p_(max) ^(head)=p_(max) ^(follow)=60degrees. Therefore, certain embodiments use a 60 degree margin aroundhead and following seas. In other embodiments, a greater or lessermargin can be used as appropriate to specific applications.

FIG. 9A illustrates experimental results for a stand-alone PIDcontroller compared to the PID controller that is controlled by amacronavigation system that implements tacking the manner outlined abovewith bow diving as a function of the wave height H in accordance with anembodiment of the invention. FIG. 9B illustrates experimental resultsfor a stand-alone PID controller compared to the PID controller that iscontrolled by a macronavigation system that implements tacking themanner outlined above with time of travel. T as a function of the targetdirection din accordance with an embodiment of the invention.Experimental results for FIGS. 9A and 9B were generalized for waveheights H from 0 meters to 3 meters.

FIG. 9A illustrates how the addition of the tack planner in certainembodiments significantly improves the performance with respect to bowdiving. However, for small wave heights certain embodiments favor notutilizing tacking as embodiments that tack dive more often with the bowthan a stand-alone microcontroller. This is due to the increased traveldistance, which consequently results in an increased number of wavecrests to pass. The extra time of travel caused by the increased traveldistance due to the tacks is revealed in FIG. 9B. But the increased timeof travel is rather moderate and worth investing for the security gainedby the tack planner. In numerous embodiments, tacking and theappropriate tack angles p_(opt) ^(head) and p_(opt) ^(follow) areselected depending on the current sea state. In particular embodimentsin the case of a calm sea state, tacking is not necessary and p_(opt)^(head) as well as p_(opt) ^(follow) can be set to zero. However, bowdiving is significantly reduced for severe sea conditions. Moreover,FIG. 9B indicates how bow diving depends on the relation of the vessel'slength and the wave length. Thereby in certain embodiments only for aspecific region around a wave height of 1.6 m, the vessel divesconsiderably with the bow into the water. For these wave heights,shallow water can yield wave lengths similar to the vessel's length.

FIG. 10 illustrates the trajectories generated by an autonomousnavigation system that utilizes tacking in the manner outlined above tonavigate to a target 100 meters away in 18 different directions inaccordance with an embodiment of the invention. Grey areas (700)indicate the occurrence of bow diving. In the illustrated embodiment,trajectories generated by the autonomous navigation system generatetacks that avoid heading and following seas. The PID controller succeedsalso in the planning condition to steer the vessel to the desiredtargets. In certain embodiments, bow diving does not occur as frequentlyas for the stand-alone PID controller. In a number of embodiments, tackplanning generates a tack-trigger mechanism that triggers turningdepending on the wave phase.

In numerous embodiments, autonomous navigation systems integrateadaptive planning and control that can be simulated, for example withhardware in the loop (HWIL) developed by the Jet Propulsion Laboratoryheadquartered in Pasadena, Calif. HWIL simulation is a technique used inthe development and testing of complex real time embedded systemsutilizing mathematical representations for each dynamic system. FIG. 11illustrates the difference between normalized heave response betweenvessel navigation with an autonomous navigation system in accordancewith an embodiment of the invention and one without the autonomousnavigation system for a 13 meter high speed vessel operating at 35 knotsover a range of sea states as simulated in a HWIL. In the illustratedembodiment, heave responses are generally minimized with adaptive pathplanning and control relative to systems without adaptive path planningand control.

While the above description contains many specific embodiments of theinvention, these should not be construed as limitations on the scope ofthe invention, but rather as an example of one embodiment thereof.

1. A method of predicting a future sea state comprising: generating asequence of at least two 3D images of a sea surface using at least twoimage sensors; detecting peaks and troughs in the 3D images using aprocessor; identifying at least one wavefront in each 3D image basedupon the detected peaks and troughs using the processor; characterizingat least one propagating wave based upon the propagation of wavefrontsdetected in the sequence of 3D images using the processor; andpredicting a future sea state using the at least one propagating wavecharacterizing the propagation of wavefronts in the sequence of 3Dimages using the processor.
 2. The method of claim 1, wherein generatinga sequence of at least two 3D images of a sea surface uses two pairs ofimage sensors.
 3. The method of claim 1, wherein generating a sequenceof at least two 3D images of a sea surface also includes using a radarsensor in combination with the at least two image sensors.
 4. The methodof claim 1, wherein the images sensors capture black and white images.5. The method of claim 1, wherein the image sensors capture colorimages.
 6. The method of claim 1, wherein detecting peaks and troughscomprises using a random sampling process.
 7. The method of claim 1,wherein detecting peaks and troughs comprises using a least meanssquares process.
 8. The method of claim 1, wherein detecting peaks andtroughs comprises using a thresholding process.
 9. The method of claim1, wherein identifying at least one wavefront comprises using a nearestneighbor process.
 10. The method of claim 1, wherein characterizing atleast one propagating wave comprises determining the amplitude,frequency and velocity of the at least one propagating wave.
 11. Themethod of claim 1, wherein characterizing at least one propagating wavecomprises determining the direction of the at least one propagatingwave.
 12. The method of claim 1, wherein characterizing at least onepropagating wave comprises determining the wave phase of the at leastone propagating wave.
 13. The method of claim 1, further comprisingautonomously navigating a vessel based upon the predicted future seastate.
 14. A method of autonomous vessel navigation based upon apredicted sea state and target location, comprising: determining atleast one subtarget based upon the target location and the predicted seastate using a macronavigation system; communicating the at least onesubtarget to the micronavigation system; and controlling a vessel tonavigate toward the at least one subtarget using a micronavigationsystem.
 15. The method of claim 14, wherein the predicted sea statecomprises a main wave direction.
 16. The method of claim 14, wherein thepredicted sea state comprises a wave phase information.
 17. The methodof claim 14, wherein the subtarget is the target location.
 18. Themethod of claim 14, wherein the micronavigation system comprises aproportional integral derivative controller.
 19. The method of claim 14,wherein navigating toward the at least one subtarget causes the vesselto tack relative to a main wave direction.
 20. The method of claim 14,wherein the subtarget causes the vessel to navigate at 60 degreesrelative to the main wave direction.
 21. The method of claim 14, whereincontrolling the vessel comprises controlling the throttle and the rudderof the vessel.
 22. The method of claim 14, wherein the predicted seastate is predicted using at least one propagating wave characterizingpropagation of wavefronts from detected peaks and troughs in a sequenceof 3D images.
 23. A system for predicting a future sea state comprising:a sensor system configured to capture information concerning the shapeof the sea surface; a sea state processor configured to communicate withthe sensor system; wherein the sensor system and the sea state processorare configured so that the captured information is used to generate asequence of 3D images of a sea surface; wherein the sea state processoris configured to: detect peaks and troughs in the 3D images; identify atleast one wavefront in each 3D image based upon the detected peaks andtroughs; characterize at least one propagating wave based upon thepropagation of wavefronts detected in the sequence of 3D images usingthe processor; and predict a future sea state using the at least onepropagating wave characterizing the propagation of wavefronts in thesequence of 3D images using the processor.
 24. The system of claim 23,wherein the sensor system comprises two pairs of image sensors.
 25. Thesystem of claim 23, wherein the sensor system comprises a radar sensor.26. The system of claim 23, wherein the sensor system and the sea stateprocessor are configured so that the captured information is used togenerate a sequence of black and white 3D images of a sea surface. 27.The system of claim 23, wherein the sensor system and the sea stateprocessor are configured so that the captured information is used togenerate a sequence of color 3D images of a sea surface.
 28. The systemof claim 23, wherein the sea state processor is configured to detectpeaks and troughs using a random sampling process.
 29. The system ofclaim 23, wherein the sea state processor is configured to detect peaksand troughs using a least means squares process.
 30. The system of claim23, wherein the sea state processor is configured to detect peaks andtroughs using a thresholding process.
 31. The system of claim 23,wherein the sea state processor is configured to identify the at leastone wavefront using a nearest neighbor process.
 32. The system of claim23, wherein the sea state processor is configured to characterize the atleast one propagating wave by the amplitude, frequency and velocity ofthe at least one propagating wave.
 33. The system of claim 23, whereinthe sea state processor is configured to characterize the at least onepropagating wave by the direction of the at least one propagating wave.34. The system of claim 23, wherein the sea state processor isconfigured to characterize the at least one propagating wave by the wavephase of the at least one propagating wave.
 35. The system of claim 23,further comprising an autonomous vessel navigation system that utilizesthe predicted future sea state to determine vessel heading whennavigating toward a target.
 36. An autonomous vessel navigation system,comprising: a macronavigation system configured to receive a predictedsea state and a target as inputs and to generate a subtarget as anoutput; and a micronavigation system configured to receive a subtargetas an input and to generate vessel control signals as outputs; whereinthe macronavigation system is configured to: determine at least onesubtarget based upon the target location and the predicted sea state;and communicate the at least one subtarget to the micronavigationsystem; and wherein the micronavigation system is configured to generatevessel control signals that head a vessel toward the at least onesubtarget.
 37. The system of claim 36, wherein the predicted sea statecomprises a main wave direction.
 38. The system of claim 36, wherein thepredicted sea state comprises wave phase information.
 39. The system ofclaim 36, wherein the subtarget is the target location.
 40. The systemof claim 36, wherein the micronavigation system comprises a proportionalintegral derivative controller.
 41. The system of claim 36, wherein themacronavigation system is configured to cause the vessel to tackrelative to a main wave direction.
 42. The system of claim 36, whereinthe macronavigation system is configured to determine at least onesubtarget that causes vessel navigation at 60 degrees relative to themain wave direction.
 43. The system of claim 36, wherein themicronavigation system is configured to generate vessel control signalsconfigured to control the throttle and the rudder of a vessel.
 44. Thesystem of claim 36, wherein the macronavigation system is configured toreceive the predicted sea state from a system for predicting a futuresea state.